Complexity of biological scaling suggests an absence of systematic trade-offs between sensory modalities in Drosophila

T he structure of nervous and sensory systems re ﬂ ects the interactions between selection pressures and functional integration, developmental constraints, and energetic costs 1,2 . How these interactions play out over time is a central question in evolutionary neurobiology, put simply; how do brains evolve, and why do they evolve that way? To address this question, a range of data is needed, spanning development, detailed anatomy and behaviour, ideally across ecologically diverse clades of species to examine how traits change at a phylogenetic scale 3 . Recently, Keesey et al. 4 provided a taste of the kind of expansive, integrative studies required. By combining data on sensory structures across ~60 Drosophila with neuroanatomical, behavioural and developmental data from selected species, they provide an in-depth examination of the evolution of Drosophila sensory and brain structure. A central conclusion drawn from these analyses was that visual and olfactory structures appear to be consistently targeted for expansion at the expense of one another – both in peripheral and central neural tissue. This hints at a pervasive underlying constraint on the sensory biology of Drosophila imposed by developmental trade-offs in the eye-antennal imaginal disc. Such a trade-off could suggest that some trait combinations are unobtainable, potentially impacting broader ecological patterns across the genus 5 . Here, we suggest the approach taken in the analyses negates key features and major shifts in biological scaling 6 , which, when re-examined in detail, present a more complex pattern of neural diversity, inconsistent with systematic trade-offs.


Supplementary Methods
To explore the scaling relationships of the head sensory structures (Figure 1/S1), , we conducted MCMC based (two-sided) phylogenetic regression models using BayesTraits V3.0.2 1 . We selected the 'Continuous: Regression' model and MCMC based calculation of the regression parameters with a Burn-in of 750,000 and 7.5 million iterations, sampling every 7500 iterations. For all models, we repeated the analysis twice to verify that the mean likelihood is very similar to the first model run. This was always the case, indicating that the MCMC analysis was stable. The species list was adapted so that species in the data were the same as species present in the tree. That specifically meant an exclusion of D. mojavensis baja, D.
sonorenisis and D. montium, such that 59 species remained. We also decided to convert the original tree from a non-ultrametric tree to an ultrametric tree, as non-ultrametric trees have the tendency to cause biases of relatedness between longer and shorter branches.
Importantly, though, the overall conclusions remained the same between the results using the original, non-ultrametric and our ultrametric tree. To convert the tree, the penalized likelihood approach was used as implemented in the "r8s" package 2,3 , which included the number of total nucleotides that can change and three calibration time points (with time extracted from http://www.timetree.org/). "r8s" calculates the distance in time using the number of substitutions, the amount of character change from the original non-ultrametric tree and the time calibration points (as in 4 ). For soe analyses, we pruned the phylogenetic tree with functions included in the ape and geiger package 5,6 (see Supplementary Code for details).
To test for isometric scaling between ESA (eye surface area) and FSA (funiculus surface area), we determined the posterior probability of the slope (β) by making use of MCMC calculations. MCMC makes iterative estimations of regression parameters including ꞵ, for which we calculated its mean, 95% confidence intervals, as well as the frequency of it being ≧1, i.e. its posterior probability, to test the hypothesis that ꞵ = 1 ( Figure S1).
To explore scaling relationships of vision and olfaction in the brain and imaginal disc structures ( Figure 2 and S2/S3), we used (two-sided) standard regressions in R (version 4.1.1 7 ). To assess species dependent differences in scaling relationships in slope and intercept, we used smatr 8 . In single-species regressions, as well as pair-wise comparisons, we did not correct for multiple comparisons as there were several statistical limitations with the data, as noted in the main article, in particular a relatively low number of individuals per species. Plots were made using ggplot2 included in the tidyverse package 9,10 . All code is available in the Supplementary Code.
To allow appropriate controlling for allometric scaling in the imaginal disc dataset we wanted to calculate a Rest of Disc value. For this, we determined a total disc size ( Figure S3G) identical to how Keesey et al. 11 determined eye and antennal portion, i.e. by marking the outline of a maximum projection of the disk image stack and determining the area size. By subtracting eye and antennal portion from the total size, we calculated the Rest of Disc (RoD) value.

Supplementary Note 1: Assessment of scaling relationships to qualify the use of ratios
Using ratios implies that the numerator (vision in Keesey et al.'s analyses 11 ) scales in proportion to the denominator (olfaction), because it sets one size in proportion to the other.
Proportional scaling assumes that the two traits scale isometrically 12 , i.e., with a slope (β) of 1.
If they do not scale with β=1, but instead scale hyper-allometrically (β>1), large eyed species (numerator) would appear to have enlarged eyes when measured with an EF ratio, as relative to the same eye size, olfactory size would be smaller if β>1. The reverse is true for hypoallometric scaling (β<1) (see details in 12 ). An alternative way to correct for allometric scaling artifacts is to include the two sensory domains in a function alongside an allometric control in a multiple regression (e.g., vision ~ body size + olfaction), as is common practice in allometric studies 13 . With such a setting the relationship of the two sizes in question could be assessed and the presence of an inverse relationship could be directly tested. Hence, our priority was to assess whether the different structures scaled isometrically. If not, using ratios would be inappropriate, in addition to the issue of limited interpretative power ( Figure 1A).  (Figure S1A'). We therefore conclude that the use of ratios in this case is inappropriate, based on lack of isometry ( Figure S1) and the potential to hide valuable information ( Figure 1A).

Supplementary Note 2: Exploring clade specific effects
Using the phylogeny that was also displayed in the original Figure 1

Supplementary Note 3: Alternative allometric controlface width
We wanted to address the possibility that body length might be a suboptimal allometric control in our analyses as it did not scale significantly with ESA once FSA was included into the model (see beginning; ESA ~ BL + FSA). We used an alternative allometric control which was also previously employed, namely face width, or interocular space (IOS). We calculated this using the given values of head width, with eye width subtracted, and performed analogous models to the ones including BL described above. Using this alternative allometric control, we again did not find any support that would warrant the use of a ratio, nor did we find an inverse relationship. Specifically, scaling of ESA with IOS and FSA resulted into an insignificant, albeit nearly significant, relationship of IOS and ESA, and isometric relationship between ESA and FSA, but which then again would not be present upon exclusion of IOS (this would be identical to Figure S1A P<0.001). Hence, the difference in slope estimates was even larger than when using body length ( Figure 1B), and any such difference in allometric scaling again means that using a ratio is potentially problematic. A regression of the residuals of ESA and FSA revealed, similarly to body length, a positive significant relationship between residual ESA and FSA (ꞵ=0.979, tdf =56=9.407, P<0.001, as in Figure 1C).   and ADp, respectively, to ROD with species as factor. The red regression line is derived from a regression without species as factor. E shows a plot of the intercept differences from the smatr analysis for EDp and ADp in Figure 2 and a positive relationship between them. F shows species-specific regressions of the relative EDp and ADp sizes as well the regression significances based on the F statistic. G shows the different values, including a newly calculated rest of disc value.